Gender: Female 
Age: 18-24
Nationality: United Kingdom
Ethnicity: White / Caucasian 
Marital status: Single 
First language: English 
Religion: Nothing 
Education: Undergraduate degree
Mother's education: High school or below
Father's education: Graduate degree
Self-assessed socioeconomic status (1-10): 4
Home: Rent 
Employment: Employed full time
Occupation: Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information)
Mother's occupation: Clerical support workers (those who perform clerical duties such as organizing, storing, computing and retrieving information)
Father's occupation: Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Hobbies: Go to gigs;Exercise;Use social media;Watch sports 
Devices used daily: Laptop;Smartphone 
NLP tools known: Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language) 
NLP tools used: Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants) 
NLP tools they would find useful: E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
Frequency of LLM use: Sometimes
LLMs used: ChatGPT 
LLM use cases: Brainstorming;Writing (e.g., composing emails);Analyze data;Learning (e.g., explaining complex concepts in simple terms);Summarizing long texts 
Contexts of LLM use: Learning;Work;Personal 
Prompt 1: Help me write a bedtime story about a lamb
Prompt 2: Please rephrase the below email to sound more professional and authoritive
Prompt 3: Make a meal plan for the week: I have chicken, vegetables, seasonings, rice, pasta, potatoes, bread, fish, milk, cheese, eggs, wraps, salad, beef mince 
Prompt 4: Help me think of a pasta dish I can serve at a dinner party with chorizo and chicken
Prompt 5: What is an accrual in an accounting context
Prompt 6: Please give me a real life workplaces situation where accruals are used
Prompt 7: Produce an image of the word 'Monday' with a pinj background
Prompt 8: Please can youmake a format for a to-do list. I want it to have different sections such as important, weekly, daily, monthly and ad hoc. Also tick boxes
Prompt 9: Produce an image of the word 'Monday' with black letters and a light pink background
Prompt 10: Please write a cover letter for assistant accountatn to match the below job description. Focus on the skills of problem solving anf time management